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<p>This document is the API specification for the Deep Java Library (DJL).</p>

<p>The key design goal for the DJL is to simplify the use of deep learning for Java
  developers. Developers don't need to be machine learning/deep learning experts to get started.
  They can start with their existing Java expertise as a foundation to learn and use ML/DL.
  Developers can
  use their favorite IDE to build, train, and deploy their models. They can also easily integrate
  these models with their
  Java applications. They don't have to compromise on model performance, scale, or choice of GPU vs.
  CPUs.

<p>DJL is deep learning engine agnostic. Developers don't have to make a choice
  between engines when they start their project. They can switch to a different engine at any
  time.

<p>DJL provides a native Java development experience and functions just like any other regular Java
  library would.
  DJL's ergonomic API interface is designed to guide developers with best practices to accomplish
  deep learning tasks.

<p>The following is an example of how to write inference code:

<pre>
    // Assume user has a pre-trained already, they just need load it
    Model model = <b>Model.load</b>(modelDir, modelName);

    // User can implement <a href="ai/djl/translate/Translator.html"><b>Translator</b></a> interface, read <a
    href="ai/djl/translate/Translator.html"><b>Translator</b></a> for detail.
    Translator translator = new <b>MyTranslator</b>();

    // Next user need create a <b>Predictor</b>, and use <a
    href="ai/djl/inference/Predictor.html#predict-I-"><b>Predictor.predict()</b></a>
    // to get prediction.
    try (Predictor&lt;Image, DetectedObjects&gt; predictor = <b>model.newPredictor</b>(translator)) {
        DetectedObjects result = predictor.<b>predict</b>(img);
    }
</pre>

<p>More tutorials, documents, and examples can be on our <a href="https://github.com/deepjavalibrary/djl/tree/master/docs">GitHub repository</a>.

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